High-dimensional databases pose a challenge with respect to efficient access. High-dimensional indexes do not work because of the often-cited "curse of dimensionality." However, users are usually interested in querying data over a relatively small subset of the entire attribute set at a time. A potential solution is to use lower dimensional indexes that accurately represent the user access patterns. A query response using the physical database design that is developed based on a static snapshot of the query workload may significantly degrade if the query patterns change. To address these issues, we introduce a parameterizable technique to recommend indexes based on index types that are frequently used for high-dimensional data sets and to dynamically adjust indexes as the underlying query workload changes. We incorporate a query pattern change detection mechanism to determine when the access patterns have changed enough to warrant change in the physical database design. By adjusting analysis parameters, we trade off analysis speed against analysis resolution. We perform experiments with a number of data sets, query sets, and parameters to show the effect that varying these characteristics has on analysis results.

Bitmap indices have been widely used in several domains such as data warehousing and scientific applications due to their efficiency in answering certain query types over large data sets. However, their utilization has been largely limited to read-only data sets or to static snapshots of data due to the cost associated with the update and append of new data. Typically, several bitmaps are associated with each indexed attribute in a table, i.e. one for each attribute value, bin, or range. Each one of these bitmaps needs to be updated to reflect a new, appended row. Since a given table could be represented by hundreds or even thousands of bitmaps, the insertion of a single record can be prohibitively costly. In order to transfer the fast query response times offered by bitmap indices to dynamic database domains, we propose an update conscious bitmap index that provides a mechanism to quickly update bitmaps to reflect dynamic database changes. For an insert operation only the bitmaps that represent the values being inserted need to be updated. We formalize the insert and delete operations of the proposed technique and provide a cost model for bitmap updates. We compare the update conscious bitmaps to traditional bitmaps in terms of storage space, update performance, and query execution time.

This work presents a parameterized framework for static and dynamic analysis of call chains in Java components. Such analyses have a wide range of uses in tools for software understanding and testing. We also describe a test coverage tool built with these analyses and the use of the tool on a real-world test suite. Our experiments evaluate the exact precision of several instances of the framework and provide a novel approach for estimating the limits of class analysis technology for computing precise call chains.

This work discusses two non-traditional approaches for evaluating the imprecision of static analysis. The approaches are based on proofs of feasibility or infeasibility that are constructed manually by the experimenters. We also describe our initial experience with these techniques.